def iterative_masked_reconstruct(reconstruct, x, mask, iter_count,
back_prop=True, name=None):
"""
用“mask”迭代地重构“x”“iter_count”次。
这个方法将调用:func:`masked_reconstruct``iter_count`次，并将前一次迭代的输出作为下一次迭代的输入`x`。将返回最后一次迭代的输出。
Args:
reconstruct: 重构x的方法
x: 被方法重构的张量
mask: 32位整型，必须对x进行广播，指示每一个x是否要被覆盖掉
iter_count (int or tf.Tensor):迭代次数 必须大于1
back_prop (bool): 是否在所有迭代中支持反向传播?
(default :obj:`True`)
name (str): 此操作在TensorFlow图中的名称。
(default "iterative_masked_reconstruct")
Returns:
tf.Tensor: 迭代重构的x。
"""
with tf.name_scope(name, default_name='iterative_masked_reconstruct'):
# 校验迭代次数
v = TensorArgValidator('iter_count')
iter_count = v.require_positive(v.require_int32(iter_count))
# 覆盖重建
x_r, _ = tf.while_loop(
# 条件
lambda x_i, i: i < iter_count,
# 赋值 标记覆盖处重构
lambda x_i, i: (masked_reconstruct(reconstruct, x_i, mask), i + 1),
[x, tf.constant(0, dtype=tf.int32)],
back_prop=back_prop
)
return x_r
def validate_n_samples(value, name):
"""
Validate the `n_samples` argument.
Args:
value: An int32 value, a int32 :class:`tf.Tensor`, or :obj:`None`.
name (str): Name of the argument (in error message).
Returns:
int or tf.Tensor: The validated `n_samples` argument value.
Raises:
TypeError or ValueError or None: If the value cannot be validated.
"""
if is_tensor_object(value):
@contextlib.contextmanager
def mkcontext():
with tf.name_scope('validate_n_samples'):
yield
else:
@contextlib.contextmanager
def mkcontext():
yield
if value is not None:
with mkcontext():
validator = TensorArgValidator(name=name)
value = validator.require_positive(validator.require_int32(value))
return value
def test_require_positive(self):
v = TensorArgValidator('xyz')
# test static values
for o in [1e-7, 1, 1.]:
self.assertEqual(v.require_positive(o), o)
for o in [-1., -1, -1e-7, 0., 0]:
with pytest.raises(ValueError, match='xyz must be positive'):
_ = v.require_positive(o)
# test dynamic values
with self.test_session():
for o, dtype in zip([1e-7, 1, 1.],
[tf.float32, tf.int32, tf.float32]):
self.assertAllClose(
v.require_positive(tf.constant(o, dtype=dtype)).eval(), o)
for o, dtype in zip(
[-1., -1, -1e-7, 0., 0],
[tf.float32, tf.int32, tf.float32, tf.float32, tf.int32]):
with pytest.raises(Exception, match='xyz must be positive'):
_ = v.require_positive(tf.constant(o, dtype=dtype)).eval()
def iterative_masked_reconstruct(reconstruct,
x,
mask,
iter_count,
back_prop=True,
name=None):
"""
Iteratively reconstruct `x` with `mask` for `iter_count` times.
This method will call :func:`masked_reconstruct` for `iter_count` times,
with the output from previous iteration as the input `x` for the next
iteration. The output of the final iteration would be returned.
Args:
reconstruct: Function for reconstructing `x`.
x: The tensor to be reconstructed by `func`.
mask: int32 mask, must be broadcastable against `x`.
Indicating whether or not to mask each element of `x`.
iter_count (int or tf.Tensor):
Number of iterations(must be greater than 1).
back_prop (bool): Whether or not to support back-propagation through
all the iterations? (default :obj:`True`)
name (str): Name of this operation in TensorFlow graph.
(default "iterative_masked_reconstruct")
Returns:
tf.Tensor: The iteratively reconstructed `x`.
"""
with tf.name_scope(name, default_name='iterative_masked_reconstruct'):
# validate the iteration count
v = TensorArgValidator('iter_count')
iter_count = v.require_positive(v.require_int32(iter_count))
# do the masked reconstructions
x_r, _ = tf.while_loop(
lambda x_i, i: i < iter_count,
lambda x_i, i: (masked_reconstruct(reconstruct, x_i, mask), i + 1),
[x, tf.constant(0, dtype=tf.int32)],
back_prop=back_prop)
return x_r